Authors: Hai Wang, Shouhong Wang
Addresses: Sobey School of Business, Saint Mary's University, 903 Robie Street, Halifax, NS B3H 2W3, Canada. ' Charlton College of Business, University of Massachusetts Dartmouth, 285 Old Westport Road, Dartmouth, MA 02747-2300, USA
Abstract: Survey data are often incomplete. Incomplete data are often mistreated and damages of missing values in survey are often overlooked in data mining. This study proposes a classification-based data mining approach to assessing the extent of damage of missing values in survey. Using this approach, an incomplete observation is translated into fuzzy observations. These fuzzy observations are used to test the classifier that has been trained by the complete data set of the survey. The test results provide a base for discovering knowledge about the implication of missing data and the quality of the survey.
Keywords: business intelligence; data mining; knowledge discovery; data quality; incomplete data; missing values; survey data; classification; fuzzy sets; fuzzy observations.
International Journal of Business Intelligence and Data Mining, 2007 Vol.2 No.3, pp.249 - 260
Available online: 19 Oct 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article